Method and Computer Server System for Receiving and Presenting Information to a User in a Computer Network

Abstract

A method for receiving and presenting information to a user in a computer network or a computer device, comprising: the step of receiving at least one text from the computer network or the computer device, said text being tagged with a text information intensity indicator; the step of determining a user channel capacity indicator; the step of comparing said channel capacity indicator with said text information intensity indicator; and the step of presenting said text or a representation of said text to said user on said device or an a device in said computer network, wherein said presentation of said text or said representation of said text is modified by using the result of said comparison.

1 . A method for receiving and presenting information to a user in a computer network or a computer device, comprising:
 the step of receiving at least one text from the computer network or the computer device, said text being tagged with a text information intensity indicator;   the step of determining a user channel capacity indicator;   the step of comparing said channel capacity indicator with said text information intensity indicator; and   the step of presenting said text or a representation of said text to said user on said device or on a device in said computer network, wherein said presentation of said text or said representation of said text is modified by using the result of said comparison.   
     
     
         2 . The method according to  claim 1 , wherein said text information intensity and user channel capacity indicator each comprise a multitude of numerical values, each value representing a different text complexity feature. 
     
     
         3 . The method according to  claim 2 , wherein the step of comparing said indicators comprises the step of establishing a difference, for instance in the form of a positive or negative distance between each text information intensity vector representation of said numerical values with said user channel capacity vector representation of said numerical values, and comparing said differences. 
     
     
         4 . The method according to  claim 1 , wherein the step of modifying said presentation comprises the step of giving preference to texts having smaller differences between their text information intensity indicator and the user channel capacity indicator over texts having larger difference between their text information intensity indicator and the user channel capacity indicator. 
     
     
         5 . The method according to  claim 3 , further comprising the step of determining whether the difference is positive or negative, and using this determination for modifying said presentation. 
     
     
         6 . The method according to  claim 1 , wherein said user channel capacity indicator is linked to said user. 
     
     
         7 . The method according to  claim 1 , wherein said user channel capacity indicator is established by analyzing texts which said user interacts with, generates and/or opens in said computer network or on said computer device, and storing said user channel capacity indicator in said computer network or on said computer device. 
     
     
         8 . The method according to  claim 7 , wherein said user channel capacity indicator is established by analyzing said texts in the same manner as the received texts are analyzed to establish said text information intensity indicator. 
     
     
         9 . The method according to  claim 2 , wherein said text complexity features comprise at least one of the following features:
 lexical familiarity of words, for instance as defined by:
   fam=log 10 cnt( w ), 
 wherein, for a word w, cnt(w) is the term count of the word in a collection of standard, contemporary writing; 
   connectedness of words, for instance as defined by:
   con1=| A   n ( w )|=| A   n-1 ( w ) u {( qεW|r (φ,φ′)Λφε A   n-1 ( w )}|
 
   wherein |A n (w)| is the node degree in n steps to a word w, where r(φ, φ′) is a Boolean function indicating if there is any relationship between synonym set φ and synonym set φ′ in a semantic lexicon; or   as defined by:
   con2= C ( T ( w ))= C∘T ( w )= C∘[t   1   , . . . ,t   n ], 
   wherein n is the number of topics in a topic space, where each topic t indicates the extent to which a word w is associated with a topic t, and
     C ( T )=log 10 ( T·I ), 
   wherein I is a vector in topic space containing for each topic t the number of links i t  pointing to that topic, and T is a topic vector;   character density in texts, for instance as defined by:
   cha n   =f   w ( X ) with  f ( X )= H   n ( X ). 
   with  f   w ( X )=Σ i=w   N ( N−w ) −1   f∘{x   j   :j=i−w+ 1, . . . , i} 
 
   with  H   n ( X )=−Σ x1eX  . . . Σ xneX   p ( x   1   , . . . ,x   n )log 2   p ( x   1   , . . . ,x   n ),
 
 where X is an ordered collection of N characters x, X={x i : i=1, . . . , N}, w defines a window size, and p(x 1 , . . . , x n ) indicates the probability of the sequence x 1 , . . . , x n  of length n in X; 
   word density in texts, for instance as defined by:
   wor n   =f   w ( X ) with  f ( X )= H   n ( X ) 
   with  f   w ( X )=Σ i=w   N ( N−w ) f∘{x   j   :j=i−w+ 1, . . . , i} 
 
     H   n ( X )=−Σ x1eX  . . . Σ xneX   p ( x   1   , . . . ,x   n )log 2   p ( x   1   , . . . ,x   n )
 
 where X is an ordered collection of N words x, X={x i : i=1, . . . , N}, 
 w defines a window size, and p(x 1 , . . . , x n ) indicates the probability of the sequence x i , . . . , x n  of length n in X; 
   semantic density in texts, for instance as defined by
   sem= f   w ( X ) with  f ( X )= H ( T ( X ))= H∘T ( X ) 
   with  f   w ( X )=Σ i=w   n ( n−w ) −1   f∘{x   j   :j=i−w+ 1, . . . , i} 
 
   with  H ( T )=−Σ tεT   p ( t )log 2   p ( t )
 
   with  T∘X=Σ   i=1   n   T ( x   j )/ n    
   where X={x i : i=1, . . . , n} is an ordered collection of n words x;   where T∘x=[t 1 , . . . , t m ] is a topic vector for a word x defined as its relative weight for m topics t,
   wherein  p ( t )= t /(Σ i=1   n   t   i ) if  tεT;p ( t )=0 else;
 
   dependency-locality in sentences, for instance as defined by:
   loc= I ( D )=Σ dεD   L   DLT ( d )
 
   where D is a collection of dependencies d within a sentence,   wherein d contains at least two linguistic units,   wherein L DLT (d)=cnt(d, Y)=Σ yεY  cnt(d,y)   where cnt(d, y) is the number of occurrences of a new discourse referent, such as suggested by Y={noun, proper noun, verb}, in d;   surprisal of sentences, for instance as defined by:
   sur n   =PP   n ( X )=2 Hn(x)    
   with H n (X)=−Σ x1εX  . . . Σ xnεX  p(x 1 , . . . , x n )log 2 p(x 1 , . . . , x n )   where X is a sentence consisting of N words x, X={x i : i=1, . . . , N};   ratio of connectives in texts, for instance as defined by:
     P ( Y,u )=Σ yεY cnt( u,y )/cnt( u )
 
   where P(Y, u) is the ratio of words with a connective function, such as Y={subordinate conjunction}, compared to all words in u,   where u is the unit of linguistic data under analysis, cnt(u, y) is the number of occurrences of connectives in u, and cnt(u) is the total number of words in u;   cohesion of texts, for instance as defined by:
   coh n   =C   n ( X )=Σ i=1   N Σ j=max(1,i-n)   i-1 ( i−j ) −1 sim( x   i   ,x   j )
 
   wherein C n (X) is the local coherence over n nearby units,   wherein sim(x i , x j ) is a similarity function between two textual units x j  and x j ,   where X is an ordered collection of N units; and
   wherein sim( x   i   ,x   j )=|{ rεR|m ( r,x   j )Λ m ( r,x   j )}|
 
   where R is the set of referents and where m(r, x) is a Boolean function denoting true if a referent r is mentioned in a textual unit x,
   or: 
   wherein sim( x   i   ,x   j )={ T ( x   i )· T ( x   j )}/{∥ T ( x   i )∥∥ T ( x   j )∥}
 
   where ∥T(x)∥ is the norm of topic vector T(x) in topic space for a textual unit x.   
     
     
         10 . The method according to  claim 1 , wherein said user channel capacity indicators are stored locally on a device of said user, for instance as web cookies. 
     
     
         11 . The method according to  claim 1 , further comprising the step of receiving a request for information which a user inputs on a device in said computer network or on said computer device; wherein said received text or texts are retrieved in such a manner that they relate to the requested information. 
     
     
         12 . The method according to  claim 11 , wherein said method comprises providing a set of different user channel capacity indicators linked to said user and determining an appropriate user channel capacity indicator depending on an analysis of the request for information, the received information or other circumstances, such as the time of day, location or the kind of activity the user is currently undertaking. 
     
     
         13 . The method according to  claim 12 , further comprising the step of filtering and/or ranking selected texts by using the result of said comparison, and the step of presenting representations of said filtered and/or ranked texts to said user on said device or on said device in said computer network, such that said user can choose to open and/or read said texts. 
     
     
         14 . The method according to  claim 12 , wherein the step of retrieving said text or texts relating to the requested information from the computer network or the computer device is performed by a web search engine. 
     
     
         15 . A computer server system in a computer network or a computer device provided with a computer programme arranged to perform a method for receiving and presenting information to a user in said computer network, said method comprising:
 the step of receiving at least one text from the computer network or the computer device, said text being tagged with a text information intensity indicator;   the step of determining a user channel capacity indicator;   the step of comparing said channel capacity indicator with said text information intensity indicator; and   the step of presenting said text or a representation of said text to said user on said device or an a device in said computer network, wherein said presentation of said text or said representation of said text is modified by using the result of said comparison.   
     
     
         16 . The method according to  claim 2 , wherein the step of modifying said presentation comprises the step of giving preference to texts having smaller differences between their text information intensity indicator and the user channel capacity indicator over texts having larger difference between their text information intensity indicator and the user channel capacity indicator. 
     
     
         17 . The method according to  claim 3 , wherein the step of modifying said presentation comprises the step of giving preference to texts having smaller differences between their text information intensity indicator and the user channel capacity indicator over texts having larger difference between their text information intensity indicator and the user channel capacity indicator. 
     
     
         18 . The method according to  claim 9 , wherein the text complexity features comprise more than one feature. 
     
     
         19 . The method according to  claim 13 , wherein the step of retrieving said text or texts relating to the requested information from the computer network or the computer device is performed by a web search engine.